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Hence, the W-B I was stratified by age and education, but not on variables that today are considered essential for an intelligence test: gender, race, geographic region, occupation, and urban versus rural residence Yet, the stratification on age was an important contribution to psychometrics, because the practice for a long time was to treat all individuals over 16 years as constituting a single age group (Wechsler, 1958, p 86) In addition, the choice of education as a rough stratification variable was a good one because of its correlations in the 50s to low 70s with IQ (Matarazzo, 1972; Reynolds et al, 1987) The WAIS was normed on a nationwide sample of African Americans and Caucasians and was systematically stratified for ages 16 to 64 on the variables of age, gender, race, geographic region, urban versus rural residence, educational attainment, and occupational group.

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x = 91.50 90

This sample was far superior to the W-B I sample, but it did not include a stratified sample of older individuals Norms for ages 65 69, 70 74, and 75 and over were derived from 359 individuals included in a sample of 475 elderly Kansas City residents Although this elderly sample was carefully selected using probability sampling techniques, it was not stratified on the major variables used to stratify the WAIS for all other age groups and is, therefore, not ideal In contrast, the WAIS-R was stratified on all of the same key variables as the.

Lower control limit = 82.54 80 0 5 20 10 15 Observation number (hour) 1 25 1 30

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actually fall below the lower control limit. This is a very strong signal that corrective action is required in this process. If we can nd and eliminate the underlying cause of this upset, we can improve process performance considerably. Control charts are a very important application of statistics for monitoring, controlling, and improving a process. The branch of statistics that makes use of control charts is called statistical process control, or SPC. We will discuss SPC and control charts in 16.

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WAIS through age 74 Like the WAIS-R, the WAIS-III was stratified on age, gender, ethnicity, education level, and geographic region An additional 200 African American and Hispanic individuals were administered the WAIS-III without discontinue rules for item-bias analyses Further oversampling was completed so that at least 30 participants would be included in each educational level within each age group for research on the relationship between cognitive abilities and education level The WAIS-III represents the ultimate in adult norming of a Wechsler battery The W-B I sample included 50 to 70 children at each year of age between 7 and 14, and 100 children at age 15 Sample sizes for the age groups between 16 and 39 ranged from 100 to 135 (Wechsler, 1958).

Models play an important role in the analysis of nearly all engineering problems. Much of the formal education of engineers involves learning about the models relevant to speci c elds and the techniques for applying these models in problem formulation and solution. As a simple example, suppose we are measuring the ow of current in a thin copper wire. Our model for this phenomenon might be Ohm s law: Current or I E R (1-2) voltage resistance

After age 39, the size of each of the age groups decreased and was inconsistent, ranging from N = 50 at ages 55 59 to N = 91 at ages 40 44 Clearly, the younger half of the age range (16 39, N = 830) was far better represented than the older half (40 70, N = 351) In the WAIS, WAIS-R, and WAIS-III these unusual disproportions were eliminated The WAIS included 200 individuals aged 16 17, 18 19, 20 24, and 55 64, and 300 subjects aged 25 34, 35 44, and 45 54; the sample of 1,700 included exactly equal numbers of males and females at each of the seven age groups The WAIS-R sample included 1,880 individuals spread across nine age levels between 16 and 74.

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